A new biclustering technique based on crossing minimization

Clustering only the records in a database (or data matrix) gives a global view of the data. For a detailed analysis or a local view, biclustering or co-clustering is required, involving the clustering of the records and the attributes simultaneously. In this paper, a new graph-drawing-based biclustering technique is proposed based on the crossing minimization paradigm that is shown to work for asymmetric overlapping biclusters in the presence of noise. Both simulated and real world data sets are used to demonstrate the superior performance of the new technique compared with two other conventional biclustering approaches.